Intelligent feed processing quality control method and system

Abstract

The invention relates to the technical field of feed quality control, in particular to an intelligent feed processing quality control method and system. According to the method, the operation parameters and the environment temperature and humidity of the production equipment are acquired in real time, and the environment and equipment state parameter set is established, so that the influence factors of the production conditions are quantified, and an accurate basis is provided for parameter adjustment of the subsequent processing flow. In the production process, the adaptive processing flow threshold value is calculated based on the operation stability of equipment and the change trend of raw material components, and unstable product quality caused by raw material fluctuation or equipment state change is avoided. And whether key indexes in the production process are in an adaptive range or not is dynamically judged by combining the change conditions of the steam absorption rate and the granulation pressure, so that the processing flow control is finer. For trend deviation possibly occurring in the production process, the stability of the production process is analyzed by analyzing the equipment load level, the energy consumption change synchronism and the abnormal trend of processing flow parameters.

Description

Technical Field

[0001] This invention relates to the field of feed quality control technology, and in particular to an intelligent feed processing quality control method and system. Background Technology

[0002] The field of feed quality control technology encompasses the systematic management and monitoring of various stages in the feed production process, including raw material selection, formulation adjustment, production and processing optimization, and finished product testing. The core of this technology involves utilizing sensor detection, data analysis, and control strategies to ensure the accuracy of feed formulations, the stability of the processing, and the pass rate of finished products.

[0003] Among them, intelligent feed processing quality control methods refer to the dynamic monitoring and adjustment of quality at each stage of feed production based on intelligent detection methods and data analysis technology to ensure the stability and safety of feed products. This includes real-time component detection of raw materials, formula ratio calculation, automatic adjustment of processing parameters, and finished product quality analysis.

[0004] In traditional feed processing quality control, the production process relies primarily on fixed parameter settings, lacking real-time monitoring of equipment operating status and environmental factors. This results in a low degree of matching between equipment operating status and processing parameters. Since fluctuations in raw material composition are unavoidable, existing technologies typically rely on historical experience for adjustments, lacking quantitative analysis of raw material quality changes, leading to significant uncertainty in the adaptability of processing parameters. Furthermore, adjustments are usually made only after quality problems occur during production, lacking trend analysis of the production process and making it difficult to identify potential quality risks in advance. This makes product quality highly susceptible to unforeseen factors. Failure to accurately match equipment load with current processing requirements may lead to excessive energy consumption or unstable equipment operation, thereby affecting the quality of the finished feed. In terms of finished product quality testing, static assessments based on final indicators such as hardness and moisture content are often relied upon, failing to incorporate adjustments based on dynamic factors during production. This results in lag in production adjustments, an inability to quickly respond to quality fluctuations, and impacts the uniformity and durability of feed pellets. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent feed processing quality control method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent feed processing quality control method, comprising the following steps: S1: Obtain the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculate the equipment operating stability coefficient and the current environmental impact coefficient, and construct a set of environmental and equipment status parameters; S2: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. Combine the environmental and equipment status parameter set to calculate the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient, and set the processing flow parameter threshold range. S3: Based on the threshold range of the processing flow parameters, determine the granulation pressure deviation state, the steam absorption rate adaptation state, and the moisture content adaptation state respectively, and obtain the processing flow parameter adaptation analysis results. S4: Based on the processing flow parameter adaptation analysis results, analyze whether there are abnormal trends in the processing flow parameters and whether the equipment load level matches the current processing flow requirements, and obtain the production process stability analysis results; S5: Based on the stability analysis results of the production process, calculate the change in hardness of the feed pellets in the current batch, determine whether the current processing parameters meet the feed processing quality control standards, and obtain the feed processing quality control results.

[0007] As a further aspect of the present invention, the set of environmental and equipment status parameters includes an environmental impact coefficient and an equipment operation stability coefficient; the threshold range of the processing flow parameters includes the pelleting pressure fluctuation range, the steam absorption rate adjustment amount, and the raw material composition variation coefficient; the processing flow parameter adaptation analysis results include the pelleting pressure offset state, the steam absorption rate adaptation state, and the moisture content adaptation state; the production process stability analysis results include the abnormal trend of processing flow parameters, the synchronicity of equipment operation and energy consumption changes, and the equipment load level matching status; and the feed processing quality control results include pellet uniformity, steam condition adaptability, and the influence range of raw material moisture changes on pellet durability.

[0008] As a further aspect of the present invention, the step of obtaining the environmental and equipment status parameter set specifically includes: S111: Obtain the operating load and energy consumption level of the current batch of feed production equipment, call the real-time monitoring data of the equipment, extract the power consumption data of the equipment and the total energy consumption within a specified time, obtain the instantaneous load rate of the equipment, collect the current ambient temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point location, and establish the equipment energy consumption and environmental dataset. S112: Based on the aforementioned equipment energy consumption and environmental dataset, the following formula is used: ; Calculate the environmental impact factor ; in, The current temperature. For the optimal temperature, For temperature sensitivity parameters, The current humidity. For optimal humidity, For humidity sensitivity parameters, It is the natural logarithm function with base e; S113: Based on the aforementioned equipment energy consumption and environmental dataset, the following formula is used: ; Calculate the stability coefficient of the equipment operation ; in, This represents the device power consumption at time i. This represents the average power consumption of the equipment. Represents the number of sampling points; S114: Normalize the environmental impact coefficient and equipment operation stability coefficient to construct a set of environmental and equipment state parameters.

[0009] As a further aspect of the present invention, the step of obtaining the threshold range of the processing flow parameters specifically includes: S211: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, call the raw material testing record of the current batch of feed, obtain the moisture content and protein content of the raw materials of the current batch of feed, calculate the pelleting pressure fluctuation range based on the equipment operation stability coefficient of the set of environmental and equipment status parameters, calculate the steam absorption rate adjustment amount based on the environmental impact coefficient of the set of environmental and equipment status parameters, and calculate the raw material composition variation coefficient relative to historical production data based on the moisture content and protein content of the raw materials of the current batch of feed. S212: Based on the granulation pressure fluctuation range, steam absorption rate adjustment amount, and raw material composition variation coefficient, set the threshold range of processing parameters.

[0010] As a further aspect of the present invention, the steps for obtaining the processing flow parameter adaptation analysis results are specifically as follows: S311: Based on the threshold range of the processing parameters, compare the current batch granulation pressure with the granulation pressure fluctuation range, extract the pressure value at all time points during the granulation process, compare the offset rate at multiple times, and if the offset rate at a certain time exceeds the preset offset threshold, it is determined that the granulation pressure exceeds the normal range, and the granulation pressure offset state is obtained. S312: Based on the threshold range of the processing flow parameters, compare the current steam absorption rate with the steam absorption rate adjustment amount, calculate the current batch steam absorption rate and compare it with the steam absorption rate adjustment amount. If the current absorption rate is within the range of the steam absorption rate adjustment amount, it is judged as an adaptation state. If it exceeds the range, it is judged as a deviation state, and the steam absorption rate adaptation state is obtained. S313: Based on the threshold range of the processing parameters, the current moisture content is compared with the coefficient of variation of the raw material components. By extracting the moisture content data of the current batch and comparing it with the variation range of the current batch moisture content, if the current moisture variation value is less than the set coefficient of variation, the moisture content is judged to be compatible; if it exceeds the coefficient of variation, it is judged to be in a deviation state. The moisture content compatibility state is obtained. Combined with the granulation pressure deviation state and the steam absorption rate compatibility state, the processing parameters compatibility analysis results are obtained.

[0011] As a further aspect of the present invention, the steps for obtaining the stability analysis results of the production process are specifically as follows: S411: Based on the results of the processing flow parameter adaptation analysis, obtain the key processing flow parameters in the current batch production process, including granulation pressure, moisture content, and steam absorption rate, analyze whether there are abnormal trends in the processing flow parameters, and obtain the abnormal trends of the processing flow parameters. S412: Based on the moisture content adaptation status and equipment operation stability coefficient, analyze the synchronicity of equipment operation and energy consumption changes, and whether the equipment load level matches the current processing requirements. Combined with the abnormal trend of processing parameters, obtain the production process stability analysis results.

[0012] As a further aspect of the present invention, the step of obtaining the feed processing quality control result specifically includes: S511: Based on the stability analysis results of the aforementioned production process, the following formula is adopted: ; Calculate the change in pellet hardness for the current batch of feed. ; in, It is the first The hardness value at each measurement point after granulation. It is the first The hardness value at each measurement point before granulation. It is the average hardness value of all measured points before granulation. It is the average of the hardness values ​​measured at all points after granulation. This is the total number of measurement points after granulation. This is the total number of measurement points before granulation; S512: Based on the change in hardness of the current batch of feed pellets, analyze the impact of production process stability on pellet uniformity. Combined with the range of raw material moisture variation, determine whether the current processing parameters meet the feed processing quality control standards. If not, adjust the pelleting pressure setting and obtain the feed processing quality control results.

[0013] A smart feed processing quality control system, wherein the smart feed processing quality control system is used to execute the above-mentioned smart feed processing quality control method, the system comprising: The environmental and equipment status monitoring module acquires the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculates the equipment operating stability coefficient and the current environmental impact coefficient, and constructs a set of environmental and equipment status parameters. The processing parameter setting module obtains the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. It calculates the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient in combination with the environmental and equipment status parameter set, and sets the processing parameter threshold range. The processing flow parameter adaptation analysis module determines the granulation pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state based on the threshold range of the processing flow parameters, and obtains the processing flow parameter adaptation analysis results. Based on the processing flow parameter adaptation analysis results, the production process stability assessment module analyzes whether there are abnormal trends in the processing flow parameters and whether the equipment load level matches the current processing flow requirements, and obtains the production process stability analysis results. Based on the stability analysis results of the production process, the feed processing quality control module calculates the change in hardness of feed pellets in the current batch, determines whether the current processing parameters meet the feed processing quality control standards, and obtains the feed processing quality control results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention establishes a set of environmental and equipment status parameters by acquiring real-time operating parameters of production equipment and ambient temperature and humidity. This quantifies the influencing factors of production conditions, providing a precise basis for subsequent adjustments to processing parameters. During production, based on the operational stability of the equipment and the changing trends of raw material composition, adaptive processing thresholds are calculated to avoid product quality instability caused by raw material fluctuations or changes in equipment status. By combining changes in steam absorption rate and pelleting pressure, the key indicators in the production process are dynamically assessed to ensure more precise control of the processing flow. For potential trend deviations during production, the stability of the production process is analyzed by examining equipment load levels, the synchronicity of energy consumption changes, and abnormal trends in processing parameters. This allows for proactive intervention rather than just feedback, reducing the impact of production fluctuations. In the finished feed stage, the processing process meets quality control requirements by calculating changes in pellet hardness. Pelletizing pressure is adjusted according to quality control standards to improve pellet uniformity and durability. This comprehensive dynamic adjustment and real-time optimization strategy effectively ensures feed processing quality, reduces reliance on manual adjustments, improves production automation, and ensures product stability and safety. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a flowchart of step S1 of the present invention; Figure 3 This is a flowchart of step S2 of the present invention; Figure 4 This is a flowchart of step S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Please see Figure 1 This invention provides a technical solution: an intelligent feed processing quality control method, comprising the following steps: S1: Obtain the operating load and energy consumption level of the current batch of feed production equipment, and collect the current ambient temperature and humidity. Calculate the current environmental impact coefficient based on the ambient temperature and humidity, and calculate the equipment operating stability coefficient based on the equipment operating load and energy consumption level. Combine the environmental impact coefficient and the equipment operating stability coefficient to construct a set of environmental and equipment status parameters. S2: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. Calculate the pelleting pressure fluctuation range based on the equipment operation stability coefficient of the environmental and equipment status parameter set, calculate the steam absorption rate adjustment amount based on the environmental impact coefficient, and calculate the raw material composition variation coefficient relative to historical production data by combining the moisture content and protein content of the current batch. Combine the pelleting pressure fluctuation range, steam absorption rate adjustment amount and raw material composition variation coefficient to set the threshold range of processing parameters. S3: Based on the threshold range of processing parameters, compare the current batch granulation pressure with the granulation pressure fluctuation range to determine the granulation pressure deviation status, compare the current steam absorption rate with the steam absorption rate adjustment amount to determine the steam absorption rate adaptation status, compare the current moisture content with the coefficient of variation of raw material composition to determine the moisture content adaptation status, and obtain the processing parameters adaptation analysis results. S4: Based on the results of the processing flow parameter adaptation analysis, calculate the cross-validation value of raw material parameters and processing flow parameters, analyze whether there are abnormal trends in processing flow parameters, analyze the synchronization of equipment operation and energy consumption changes based on the moisture content adaptation status and equipment operation stability coefficient, and whether the equipment load level matches the current processing flow requirements, to obtain the production process stability analysis results. S5: Based on the results of the production process stability analysis, calculate the change in hardness of feed pellets in the current batch, analyze the impact of production process stability on pellet uniformity, including the suitability of steam conditions for pellet uniformity and the range of influence of raw material moisture changes on pellet durability, determine whether the current processing parameters meet the feed processing quality control standards, and if not, adjust the pelleting pressure setting to obtain the feed processing quality control results. The environmental and equipment status parameter set includes environmental impact coefficient and equipment operation stability coefficient. The processing flow parameter threshold range includes pelleting pressure fluctuation range, steam absorption rate adjustment amount, and raw material composition variation coefficient. The processing flow parameter adaptation analysis results include pelleting pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state. The production process stability analysis results include abnormal trends of processing flow parameters, synchronicity of equipment operation and energy consumption changes, and equipment load level matching. The feed processing quality control results include pellet uniformity, steam condition adaptability, and the range of influence of raw material moisture changes on pellet durability.

[0018] Please see Figure 2The specific steps for obtaining the environmental and equipment status parameter set are as follows: S111: Obtain the operating load and energy consumption level of the current batch of feed production equipment, call the real-time monitoring data of the equipment, extract the power consumption data of the equipment and the total energy consumption within a specified time, obtain the instantaneous load rate of the equipment, collect the current ambient temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point location, and establish the equipment energy consumption and environmental dataset. First, a power sensor is installed on the feed production equipment. This sensor can measure the instantaneous power of the equipment in real time and record energy consumption data at set time intervals. For example, with a monitoring cycle of 10 seconds, the power value (unit: kilowatts) of the equipment is acquired once per cycle. This data is stored in a local database or a cloud system. The acquired dataset includes a timestamp, equipment number, and the power reading at the corresponding moment. For example, in a batch of feed production, the initial power consumption of each piece of equipment is 5.2kW, the maximum monitored power consumption is 7.8kW, and the minimum power consumption is 4.9kW. Based on this dataset, the total energy consumption per unit time can be calculated as follows: Within each time interval, the energy consumption can be calculated by multiplying the power by the time. For example, if the power value is 6.5kW within a 10-second sampling cycle, then the energy consumption for that time period is... Accumulate the energy consumption over all time intervals to obtain the total energy consumption data of the equipment during the production process of that batch. For example, in a 1-hour production process, the total energy consumption is... It can be calculated as ,in, The total energy consumption data represents the power consumption of the device at time i. This data can serve as the basis for energy consumption optimization analysis. Meanwhile, the instantaneous load rate can be calculated by the ratio of the current power consumption to the device's rated power. For example, if the device's rated power is 8.0kW, the load rate at a certain time is calculated as follows: This value represents the load level of the equipment at this moment. When collecting ambient temperature and humidity, temperature and humidity sensors installed in the production workshop are used to record temperature and humidity values ​​every 30 seconds. For example, the temperature recorded at a certain moment is 28.5℃ and the humidity is 65.2%. Combined with equipment power consumption data and environmental data, a data set of equipment power consumption and environment is formed.

[0019] S112: Based on equipment energy consumption and environmental datasets, the following formula is used: ; Calculate the environmental impact factor ; in, The current temperature (°C); The optimal temperature (°C) is the temperature most favorable for the production process. This is a temperature sensitivity parameter, representing the degree to which the processing flow is affected when the temperature deviates from the optimal temperature; Current humidity (%); The optimal humidity (%) is the humidity that is most beneficial to the production process; This is a humidity sensitivity parameter, representing the degree to which changes in humidity affect production. It is the natural logarithm function with base e.

[0020] First, define the following parameters: This is the current ambient temperature, for example, 30°C.

[0021] The optimal temperature is assumed to be 25°C.

[0022] This is a temperature sensitivity parameter, assumed to be 5°C.

[0023] This is the current measured ambient humidity, for example, 75%.

[0024] The optimal humidity is assumed to be 60%.

[0025] This is a humidity sensitivity parameter, assumed to be 10%.

[0026] Calculate the environmental impact factor : ; Substitute the values ​​into the formula: ; Calculate each part: Temperature section: ; Humidity section: ; Final calculation : ; The obtained environmental impact coefficient Approximately 0.5557, this result indicates the degree of deviation between the current environmental conditions and the optimal conditions. Lower... A value of 0 indicates a small environmental impact, while a higher value suggests that environmental or equipment parameters need to be adjusted to meet production requirements.

[0027] S113: Based on device energy consumption and environmental datasets, the following formula is used: ; Calculate the stability coefficient of the equipment operation ; in, This represents the device power consumption at time i. This represents the average power consumption of the equipment. This represents the number of sampling points.

[0028] Suppose that in a certain batch of production, power data is monitored every 10 seconds to obtain a power consumption dataset. (Unit: kW), calculate the average power consumption. : Then calculate the power consumption offset rate at each time step: This value represents the stability of the equipment operation. A higher value indicates that the power consumption fluctuates greatly, which may be due to problems such as unbalanced equipment load. The final result is the equipment operation stability coefficient.

[0029] S114: Normalize the environmental impact coefficient and equipment operation stability coefficient to construct a set of environmental and equipment state parameters; First, determine the environmental impact factor. and equipment operating stability coefficient The benchmark range, the benchmark range of the environmental impact factor This was determined through analysis of historical production data. Based on data from multiple production batches, the relationship between temperature and humidity deviations and production stability was statistically analyzed. When the environmental impact coefficient is below 0.2, the production and processing flow fluctuates less, and feed quality remains stable. Therefore, this was set... As a reference range, the baseline range for the equipment's operational stability coefficient is... By analyzing equipment energy consumption fluctuations and monitoring operating data from multiple batches of equipment, the power consumption fluctuation rate was calculated. When the value is below 0.1, the equipment operates stably, and power fluctuations will not have a significant impact on production; therefore, this value is selected. This serves as a benchmark range for the equipment's operational stability coefficient.

[0030] Then, the environmental impact coefficient was calculated. and equipment operating stability coefficient The normalization process is performed, and the calculation method is as follows: ; in, This represents the normalized environmental impact coefficient. This represents the normalized equipment operating stability coefficient. Normalization allows for direct comparison of data with different dimensions. Substitute the calculation results into: ; Then calculate the comprehensive impact factor of environment and equipment. We will use the average of the two: ; This value is used to measure the overall impact of the current environment and equipment operating status. If a certain threshold is exceeded, such as 0.8, it may be necessary to adjust environmental parameters or optimize the operating status of the equipment, and finally construct a set of environmental and equipment status parameters.

[0031] Please see Figure 3 The specific steps for obtaining the threshold range of the processing parameters are as follows: S211: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, retrieve the raw material testing records of the current batch of feed, and use the formula based on the equipment operating stability coefficient of the set of environmental and equipment status parameters: ; Calculate the granulation pressure fluctuation range ; in, This represents the average granulation pressure. This represents the stability coefficient of the equipment operation.

[0032] The system acquires the pelleting pressure and steam absorption rate during the current batch of feed production, as well as the moisture and protein content of the feed ingredients. It retrieves data from the pressure sensor of the feed pellet mill, extracts the pelleting pressure over different time periods, and calculates the average pressure and its fluctuation range. In actual production, high-precision sensors are typically used to collect pellet mill pressure data once per second. For example, the pressure sensor data recorded by a certain pellet mill during the production of a specific batch might look like this: Table 1 Granulation Pressure Data Recording Table ; The average pressure was calculated from this data: Simultaneously, the fluctuation range between the maximum and minimum values ​​is calculated to obtain the maximum pressure. MPa, minimum pressure MPa, fluctuation range: Finally, considering the equipment's operational stability coefficient The formula for calculating the granulation pressure fluctuation range is: Finally, the granulation pressure fluctuation range is obtained.

[0033] The environmental impact coefficient, based on the set of environmental and equipment state parameters, is calculated using the following formula: ; Calculate the steam absorption rate adjustment amount ; in, This represents the standard steam absorption rate.

[0034] The current batch of steam supply data, measured by the flow meter, is as follows: Table 2. Record of Steam Supply and Absorption ; Calculate the standard steam absorption rate: ; Combining environmental impact coefficient The steam absorption rate adjustment is calculated using the following formula: Finally, the steam absorption rate adjustment amount is obtained.

[0035] Based on the moisture and protein content of the current batch of feed ingredients, the formula is used: ; Calculate the coefficient of variation of raw material composition relative to historical production data. ; in, Representing the Moisture content or protein content of batch of raw materials. Represents the maximum value. Represents the minimum value. Represents the number of samples.

[0036] The current batch production data, including recorded moisture and protein content, is as follows: Table 3. Record of Raw Material Composition Content ; The coefficient of variation of raw material composition is calculated using the following formula: The coefficient of variation for moisture content is calculated as follows: The coefficient of variation for protein content is calculated as follows: Finally, the coefficient of variation of the raw material composition is obtained.

[0037] S212: Set the threshold range of processing parameters by combining the granulation pressure fluctuation range, steam absorption rate adjustment amount and raw material composition variation coefficient; Based on the pelleting pressure fluctuation range, steam absorption rate adjustment, and raw material composition variation coefficient, threshold ranges for processing parameters are set. First, based on the pelleting pressure fluctuation range, an allowable pressure range is set. During feed pelleting, pressure directly affects pellet density and strength. If the pressure is too low (below 1.16 MPa), pellets may not form properly, leading to an increased powder yield. If the pressure is too high (above 1.36 MPa), pellet hardness may be excessive, affecting feed palatability. Therefore, a range of [1.16, 1.36] MPa is set to maintain stable pellet quality. Second, based on the steam absorption rate adjustment, a target steam flow range is set. Steam absorption rate reflects the moisture and heat acquisition during feed conditioning. A low absorption rate (below 0.78) may... Insufficient feed temperature affects starch gelatinization rate, which in turn affects pellet strength. Excessively high absorption rate (above 0.83) may lead to excessive feed moisture content, increasing the risk of mold growth during storage. Therefore, an absorption rate range of [0.78, 0.83] is set. Finally, based on the coefficient of variation of raw material components, the allowable error range of the raw material ratio is adjusted. A moisture content coefficient of variation of 0.0328 indicates that the moisture fluctuation of the current batch is small; therefore, the moisture content control range is set at [11.8%, 12.6%]. A protein content coefficient of variation of 0.0261 indicates that the protein fluctuation is small; the allowable protein range is set at [15.0%, 15.6%]. This range ensures that the nutritional components of the feed fluctuate within a reasonable range. Finally, by integrating all parameters, a threshold range for the processing parameters is established.

[0038] Please see Figure 4 The specific steps for obtaining the processing flow parameter adaptation analysis results are as follows: S311: Based on the threshold range of the processing parameters, compare the current batch granulation pressure with the granulation pressure fluctuation range, extract the pressure value at all time points during the granulation process, compare the offset rate at multiple times, and if the offset rate at a certain time exceeds the preset offset threshold, it is determined that the granulation pressure is out of normal range, and the granulation pressure offset status is obtained. Based on the threshold range of processing parameters, the granulation pressure of the current batch is compared with the granulation pressure fluctuation range to obtain the granulation pressure data of the current batch, and the pressure value at each time point during the granulation process is extracted. For example, the pressure record of a certain batch of production is as follows: Table 4. Record of Granulation Pressure Data for Current Batch ; The current batch data is compared with the granulation pressure fluctuation range [1.16, 1.36] MPa to determine the granulation pressure deviation. The deviation rate is calculated using the following formula: ; in, MPa, calculate the offset rate at a certain moment (e.g., 10s): ; in, This represents the granulation pressure offset rate. Represents the granulation pressure at the current moment. This parameter represents the average value of the granulation pressure fluctuation range and is used to determine whether the current pressure exceeds the set range. If the offset rate at a certain moment exceeds the preset offset threshold, such as 0.1, it is determined that the granulation pressure is outside the normal range. If all time points are within this range, it is determined that the pressure is suitable, and finally the granulation pressure offset status is obtained.

[0039] S312: Based on the threshold range of the processing parameters, compare the current steam absorption rate with the steam absorption rate adjustment amount, calculate the current batch steam absorption rate and compare it with the steam absorption rate adjustment amount. If the current absorption rate is within the range of the steam absorption rate adjustment amount, it is judged as an adaptation state. If it exceeds the range, it is judged as a deviation state, and the steam absorption rate adaptation state is obtained. The current steam absorption rate is compared with the steam absorption rate adjustment amount to determine the steam absorption rate adaptation status. The steam absorption rate of the current batch is extracted, and the actual steam absorption of the feed during the production process of this batch is calculated. The steam absorption rate of the current batch is calculated as follows: Table 5. Current Batch Steam Absorption Rate Data Record Table ; Calculate the steam absorption rate for the current batch: ; ; in, Represents the current steam absorption rate. This represents the amount of steam absorbed at time k. The value represents the steam supply at time k. The summation symbol represents the accumulated steam data over 10 time points, used to calculate the overall steam absorption rate.

[0040] The value is compared with the steam absorption rate adjustment amount [0.78, 0.83]. If the current absorption rate is within the range, it is judged as an adapted state. If it is outside the range, it is judged as a deviation state. Finally, the steam absorption rate adapted state is obtained.

[0041] S313: Based on the threshold range of processing parameters, the current moisture content is compared with the coefficient of variation of raw material components. By extracting the moisture content data of the current batch and comparing it with the variation range of the current batch moisture content, if the current moisture variation value is less than the set coefficient of variation, the moisture content is judged to be suitable. If it exceeds the coefficient of variation, it is judged to be in a deviation state, and the moisture content suitability state is obtained. Combined with the granulation pressure deviation state and the steam absorption rate suitability state, the processing parameters suitability analysis results are obtained. The current moisture content is compared with the coefficient of variation of the raw material composition to determine the moisture content suitability. The moisture content data of the current batch is extracted and compared with the range of variation of the current batch moisture content. Assume the moisture content of the current batch is recorded as follows: Table 6. Current Batch Moisture Content Data Record Table ; Calculate the range of variation in moisture content for the current batch: ; ; in, This represents the coefficient of variation of moisture content in the current batch. This represents the moisture content of the kth batch. Represents the maximum moisture content. Represents the minimum moisture content. This represents the number of sample batches. This formula calculates the fluctuation of the moisture content of the current batch and determines its suitability.

[0042] The value is compared with the coefficient of variation of the raw material composition [0.0328]. If the current moisture variation value is less than the set coefficient of variation, the moisture content is judged to be suitable. If it exceeds the value, it is judged to be in a deviation state. Finally, the moisture content suitability state is obtained, and the processing flow parameter suitability analysis results are obtained by combining all parameters.

[0043] Please see Figure 5 The specific steps for obtaining the stability analysis results of the production process are as follows: S411: Based on the results of the processing flow parameter adaptation analysis, obtain the key processing flow parameters in the current batch production process, including granulation pressure, moisture content, and steam absorption rate, analyze whether there are abnormal trends in the processing flow parameters, and obtain the abnormal trends of the processing flow parameters. Historical data of key processing parameters during the current batch production process are obtained, including granulation pressure, moisture content, and steam absorption rate. Their changing trends across different production batches are calculated. The processing parameters for the current batch are extracted, and the rate of change of these parameters is calculated using a moving average method. A threshold range is set to determine if there are any abnormal trends in the current processing parameters, using the following formula: ; ; in, This represents the offset rate of the processing parameters. This represents the processing parameters for the current batch. This represents the processing parameter value for batch g. The summation symbol represents the number of sample batches. The summation symbol indicates that the data from q batches are used to calculate the overall trend. This formula is used to determine whether the current processing parameters exceed the normal range of variation.

[0044] If the M offset exceeds the preset threshold (e.g., 0.03), it is determined that there is an abnormal trend in the processing parameters, and the abnormal trend status of the processing parameters is finally obtained.

[0045] S412: Based on the moisture content adaptation status and equipment operation stability coefficient, analyze the synchronicity of equipment operation and energy consumption changes, and whether the equipment load level matches the current processing flow requirements. Combine the abnormal trends of processing flow parameters to obtain the production process stability analysis results. Extract the power consumption data of the current batch of equipment and, in conjunction with the changes in moisture content, calculate the impact of moisture content adaptation on equipment operation using the following formula: ; ; in, This represents the synchronicity between equipment operation and changes in energy consumption. This represents the power consumption of the current batch of equipment. This represents the average power consumption of the equipment. This represents the moisture content of the current batch. This represents the average moisture content. This formula is used to assess the impact of moisture content fluctuations on equipment operation and to determine whether the equipment load level matches the processing requirements, ultimately establishing the results of the production process stability analysis.

[0046] If Z synchronization exceeds the set threshold (e.g., 0.05), it indicates that the equipment operation and energy consumption changes are not synchronized. At the same time, the equipment load level offset rate is calculated. If the equipment load offset rate exceeds the preset range, it is determined that the equipment operation status is abnormal, and the final production process stability analysis result is obtained.

[0047] Please see Figure 6 The specific steps for obtaining feed processing quality control results are as follows: S511: Based on the results of the production process stability analysis, the following formula is used: ; Calculate the change in pellet hardness for the current batch of feed. ; in, It is the first The hardness value at each measurement point after granulation. It is the first The hardness value at each measurement point before granulation. It is the average hardness value of all measured points before granulation. It is the average of the hardness values ​​measured at all points after granulation. This is the total number of measurement points after granulation. This represents the total number of measurement points before granulation.

[0048] Given the measurement data: Hardness value before granulation: N; Hardness value after granulation: N; Calculate the average: ; ; calculate : ; This result It indicates the change in hardness during the granulation process, providing scientific data support for quality monitoring and process optimization in the production process.

[0049] S512: Based on the change in hardness of feed pellets in the current batch, analyze the impact of production process stability on pellet uniformity. Combined with the range of raw material moisture variation, determine whether the current processing parameters meet the feed processing quality control standards. If not, adjust the pelleting pressure setting and obtain the feed processing quality control results. First, it is necessary to obtain steam condition data, including steam pressure (MPa) and temperature (°C). Let's assume the currently measured steam pressure is... MPa, steam temperature is Then, based on steam condition data, the distribution range of particle hardness is calculated. The appropriate ranges for particle hardness under different steam parameters are set as follows: Table 7 Distribution of Steam Pressure and Feed Pellet Hardness ; As shown in Table 1, the hardness variation range under different steam pressures shows a clear trend. The hardness variation measured under the current processing conditions is... N, and the table The change in hardness under steam conditions corresponding to MPa is consistent, indicating a high degree of steam compatibility.

[0050] Further analysis is conducted by considering the range of raw material moisture content variations. Assuming the current batch of raw material has a moisture content of 12% before and 10% after granulation, the range within which moisture variation affects particle durability is calculated. The effect of moisture on durability can be expressed as: ; in, The effect of moisture change on pellet durability (unit: %) indicates the degree to which changes in moisture content during feed pelleting affect pellet quality and stability. Moisture content of raw materials before granulation (unit: %), refers to the proportion of moisture in the raw materials before granulation, currently measured at 12%. Moisture content of pellets after pelleting (unit: %) refers to the proportion of moisture contained in feed pellets after pelleting. The current measurement value is 10%.

[0051] If the collected parameters are, , Substitute the values ​​into the calculation: ; The durability impact is 16.67%, indicating that the reduction in raw material moisture content has a certain impact on particle durability. The granulation pressure setting needs to be adjusted in conjunction with processing parameters. If the current pressure is too low, resulting in uneven hardness distribution, the pressure can be appropriately adjusted. The pressure (MPa) will be monitored, and feed processing quality will continue to be monitored to obtain the final feed processing quality control results. The impact of a 16.67% decrease in moisture on pellet durability needs to be analyzed in conjunction with the effect of moisture on the physical properties of feed pellets. Moisture plays a role in binding, lubrication, and plasticization during feed pelleting, affecting the hardness, durability, and disintegration rate of the pellets. Excessive or insufficient moisture reduction may affect the final product quality. The following is a hypothetical analysis: When the moisture reduction is moderate (usually within the range of 10%-20%), the binding properties of the feed pellets can still be maintained, the pellet hardness is relatively stable, and it is not easily broken. If the moisture reduction is too large (>25%), it may lead to a decrease in the internal binding force of the pellets, making the pellets brittle, reducing durability, and making them prone to breakage during transportation or storage. If the moisture reduction is too small (<5%), the pellet moisture content is too high, which may lead to softening of the pellets, or even spoilage due to the growth of mold or bacteria during storage.

[0052] An intelligent feed processing quality control system is provided, which is used to execute the above-mentioned intelligent feed processing quality control method. The system includes: The environmental and equipment status monitoring module acquires the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculates the equipment operating stability coefficient and the current environmental impact coefficient, and constructs a set of environmental and equipment status parameters. The processing parameters setting module obtains the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. It calculates the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient in combination with the environmental and equipment status parameter set, and sets the processing parameters threshold range. The processing flow parameter adaptation analysis module determines the granulation pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state based on the threshold range of processing flow parameters, and obtains the processing flow parameter adaptation analysis results. The production process stability assessment module analyzes whether there are abnormal trends in the processing parameters and whether the equipment load level matches the current processing requirements based on the processing parameters adaptation analysis results, and obtains the production process stability analysis results. Based on the stability analysis results of the production process, the feed processing quality control module calculates the change in the hardness of the feed pellets in the current batch, determines whether the current processing parameters meet the feed processing quality control standards, and obtains the feed processing quality control results.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent feed processing quality control, characterized in that, Includes the following steps: S1: Obtain the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculate the equipment operating stability coefficient and the current environmental impact coefficient, and construct a set of environmental and equipment status parameters; S2: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. Combine the environmental and equipment status parameter set to calculate the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient, and set the processing flow parameter threshold range. S3: Based on the threshold range of the processing flow parameters, determine the granulation pressure deviation state, the steam absorption rate adaptation state, and the moisture content adaptation state respectively, and obtain the processing flow parameter adaptation analysis results. S4: Based on the processing flow parameter adaptation analysis results, analyze whether there are abnormal trends in the processing flow parameters and whether the equipment load level matches the current processing flow requirements, and obtain the production process stability analysis results; S5: Based on the stability analysis results of the production process, calculate the change in hardness of the feed pellets in the current batch, determine whether the current processing parameters meet the feed processing quality control standards, and obtain the feed processing quality control results.

2. The intelligent feed processing quality control method according to claim 1, characterized in that, The set of environmental and equipment status parameters includes environmental impact coefficient and equipment operation stability coefficient. The threshold range of the processing parameters includes the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and raw material composition variation coefficient. The processing parameters adaptation analysis results include pelleting pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state. The production process stability analysis results include abnormal trends of processing parameters, synchronicity of equipment operation and energy consumption changes, and equipment load level matching. The feed processing quality control results include pellet uniformity, steam condition adaptability, and the range of influence of raw material moisture changes on pellet durability.

3. The intelligent feed processing quality control method according to claim 2, characterized in that, The specific steps for obtaining the environmental and equipment status parameter set are as follows: S111: Obtain the operating load and energy consumption level of the current batch of feed production equipment, call the real-time monitoring data of the equipment, extract the power consumption data of the equipment and the total energy consumption within a specified time, obtain the instantaneous load rate of the equipment, collect the current ambient temperature and humidity, extract the temperature and humidity sensor data, record the measurement time and measurement point location, and establish the equipment energy consumption and environmental dataset. S112: Based on the aforementioned equipment energy consumption and environmental dataset, the following formula is used: ； Calculate the environmental impact factor ; in, The current temperature. For the optimal temperature, Current temperature Sensitivity parameters, The current humidity. For optimal humidity, Current humidity Sensitivity parameters, It is the natural logarithm function with base e; S113: Based on the aforementioned equipment energy consumption and environmental dataset, the following formula is used: ； Calculate the stability coefficient of the equipment operation ; in, Representing the The device power consumption at any given time This represents the average power consumption of the equipment. Represents the number of sampling points; S114: Normalize the environmental impact coefficient and equipment operation stability coefficient to construct a set of environmental and equipment state parameters.

4. The intelligent feed processing quality control method according to claim 3, characterized in that, The specific steps for obtaining the threshold range of the processing parameters are as follows: S211: Obtain the pelleting pressure and steam absorption rate during the production process of the current batch of feed, call the raw material testing record of the current batch of feed, obtain the moisture content and protein content of the raw materials of the current batch of feed, calculate the pelleting pressure fluctuation range based on the equipment operation stability coefficient of the set of environmental and equipment status parameters, calculate the steam absorption rate adjustment amount based on the environmental impact coefficient of the set of environmental and equipment status parameters, and calculate the raw material composition variation coefficient relative to historical production data based on the moisture content and protein content of the raw materials of the current batch of feed. S212: Based on the granulation pressure fluctuation range, steam absorption rate adjustment amount, and raw material composition variation coefficient, set the threshold range of processing parameters.

5. The intelligent feed processing quality control method according to claim 4, characterized in that, The specific steps for obtaining the processing flow parameter adaptation analysis results are as follows: S311: Based on the threshold range of the processing parameters, compare the current batch granulation pressure with the granulation pressure fluctuation range, extract the pressure value at all time points during the granulation process, compare the offset rate at multiple times, and if the offset rate at a certain time exceeds the preset offset threshold, it is determined that the granulation pressure exceeds the normal range, and the granulation pressure offset state is obtained. S312: Based on the threshold range of the processing flow parameters, compare the current steam absorption rate with the steam absorption rate adjustment amount, calculate the current batch steam absorption rate and compare it with the steam absorption rate adjustment amount. If the current absorption rate is within the range of the steam absorption rate adjustment amount, it is judged as an adaptation state. If it exceeds the range, it is judged as a deviation state, and the steam absorption rate adaptation state is obtained. S313: Based on the threshold range of the processing parameters, the current moisture content is compared with the coefficient of variation of the raw material components. By extracting the moisture content data of the current batch and comparing it with the variation range of the current batch moisture content, if the current moisture variation value is less than the set coefficient of variation, the moisture content is judged to be compatible; if it exceeds the coefficient of variation, it is judged to be in a deviation state. The moisture content compatibility state is obtained. Combined with the granulation pressure deviation state and the steam absorption rate compatibility state, the processing parameters compatibility analysis results are obtained.

6. The intelligent feed processing quality control method according to claim 5, characterized in that, The specific steps for obtaining the stability analysis results of the production process are as follows: S411: Based on the results of the processing flow parameter adaptation analysis, obtain the key processing flow parameters in the current batch production process, including granulation pressure, moisture content, and steam absorption rate, analyze whether there are abnormal trends in the processing flow parameters, and obtain the abnormal trends of the processing flow parameters. S412: Based on the moisture content adaptation status and equipment operation stability coefficient, analyze the synchronicity of equipment operation and energy consumption changes, and whether the equipment load level matches the current processing requirements. Combined with the abnormal trend of processing parameters, obtain the production process stability analysis results.

7. The intelligent feed processing quality control method according to claim 6, characterized in that, The specific steps for obtaining the feed processing quality control results are as follows: S511: Based on the stability analysis results of the aforementioned production process, the following formula is adopted: ； Calculate the change in pellet hardness for the current batch of feed. ; in, It is the first The hardness value at each measurement point after granulation. It is the first The hardness value at each measurement point before granulation. It is the average hardness value of all measured points before granulation. It is the average of the hardness values ​​measured at all points after granulation. This is the total number of measurement points after granulation. This is the total number of measurement points before granulation; S512: Based on the change in hardness of the current batch of feed pellets, analyze the impact of production process stability on pellet uniformity. Combined with the range of raw material moisture variation, determine whether the current processing parameters meet the feed processing quality control standards. If not, adjust the pelleting pressure setting and obtain the feed processing quality control results.

8. An intelligent feed processing quality control system, characterized in that, The intelligent feed processing quality control method according to any one of claims 1-7, wherein the system comprises: The environmental and equipment status monitoring module acquires the operating parameters of the current batch of feed production equipment and the current ambient temperature and humidity, calculates the equipment operating stability coefficient and the current environmental impact coefficient, and constructs a set of environmental and equipment status parameters. The processing parameter setting module obtains the pelleting pressure and steam absorption rate during the production process of the current batch of feed, as well as the moisture content and protein content of the feed raw materials in the current batch. It calculates the pelleting pressure fluctuation range, steam absorption rate adjustment amount, and feed raw material composition variation coefficient in combination with the environmental and equipment status parameter set, and sets the processing parameter threshold range. The processing flow parameter adaptation analysis module determines the granulation pressure deviation state, steam absorption rate adaptation state, and moisture content adaptation state based on the threshold range of the processing flow parameters, and obtains the processing flow parameter adaptation analysis results. Based on the processing flow parameter adaptation analysis results, the production process stability assessment module analyzes whether there are abnormal trends in the processing flow parameters and whether the equipment load level matches the current processing flow requirements, and obtains the production process stability analysis results. Based on the stability analysis results of the production process, the feed processing quality control module calculates the change in hardness of feed pellets in the current batch, determines whether the current processing parameters meet the feed processing quality control standards, and obtains the feed processing quality control results.

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